The present invention relates to fee-based printing and, more particularly, to estimating the amount of color ink that will be consumed printing a print job element in support of a pay-for-print service with differential pricing.
Differential pricing in pay-for-print services has conventionally been limited to a binary pricing model that charges a customer a base rate for a black-and-white page and a premium rate for a color page. However, the true cost of printing correlates with the types and amounts of the respective inks used to print the page. Since the binary pricing model fails to take into account the amounts of the respective inks used, it inevitably results in some customers being overcharged and other customers being undercharged. For example, under the binary pricing model, a first customer who wants a color logo printed on the corner of an otherwise black-and-white page pays the rate same rate as a second customer who wants a color photograph to span the page, even though the true cost to print the first customer's page is much lower than the true cost to print the second customer's page. Under the binary pricing model, the first customer therefore subsidizes the second customer.
A considerable obstacle to differential pricing models that more accurately reflect the true cost of print jobs has been difficulty in estimating the amount of color ink used. One known approach to estimating color ink usage is to fully rasterize an image into an engine-ready bitmap and then examine each pixel in the bitmap to determine how much color ink a printed page having the image will require. This approach is highly accurate; however, since this approach requires the image to be fully rasterized before conducting the review and inspects every pixel it can be unduly time consuming. Another known approach to estimating color ink usage is to construct a reduced resolution bitmap of an image (e.g. thumbnail), examine each pixel of the reduced resolution bitmap and interpolate how much color ink a printed page having the image will require. This approach is less accurate and still requires partial rasterization of the image, which can lead to substantial delays in providing the estimate.